Understanding Algorithms For Big Data Compsci 229r Lecture 10
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 10. Randomized and approximate F0 lower bounds, disjointness, Fp lower bound, dimensionality reduction (JL lemma).
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 10
- Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
- MapReduce: TeraSort, minimum spanning tree, triangle counting.
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 10
Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma. External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
Online primal/dual: e/(e-1) ski rental, set cover; approximation
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 10.